Learning Chemical Reaction Networks

Related lectures (30)

Page 1 of 3

Explores maximum likelihood inference, model selection, and comparing models using likelihood ratios.

Explores models, learning paradigms, and applications in Mathematics of Data.

Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.

Covers unsupervised learning, dimensionality reduction, SVD, low-rank estimation, PCA, and Monte Carlo Markov Chains.

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Explores learning and adaptive control for robots through SEDS and LPV-DS, emphasizing stability, non-linear dynamics, and optimization.

Covers variational inference and neural networks for classification tasks.

Covers dimensionality reduction, PCA, clustering techniques, and density estimation methods.

Covers dimensionality reduction, clustering, and density estimation techniques, including PCA, K-means, GMM, and Mean Shift.

Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.